## Advice for Undergraduate Students

2nd Year Students in **2020/21** *(PDF, 120 KB)*

3rd & 4th Year Students in **2020/21** *(PDF, 105 KB)*

## Undergraduate STAT Courses

**200 Level Courses**

**STAT 263**- Introduction to Statistics

A basic course in statistical methods with the necessary probability included. Topics include probability models, random

variables, distributions, estimation, hypothesis testing, elementary nonparametric methods.

NOTE Also offered online. Consult Arts and Science Online. Learning Hours may vary.**LEARNING HOURS** 120 (36L;84P)**RECOMMENDATION** An Ontario 4U mathematics course or equivalent.**EXCLUSION** No more than 3.0 units from BIOL 243/3.0; CHEE 209/3.0; ECON 250/3.0; GPHY 247/3.0; KNPE 251/3.0; NURS

323/3.0; POLS 385/3.0; PSYC 202/3.0; SOCY 211/3.0; STAT 263/3.0; STAT 267/3.0; STAT 367/3.0; COMM 162/3.0.**ONE‐WAY EXCLUSION** May not be taken with or after STAT 269/3.0.

**STAT 268**- Statistics & Probability I

Basic ideas of probability theory such as random experiments, probabilities, random variables, expected values,

independent events, joint distributions, conditional expectations, moment generating functions. Main results of probability

theory including Chebyshev’s inequality, law of large numbers, central limit theorem. Introduction to statistical computing.**LEARNING HOURS** 120 (36L;84P)**PREREQUISITE** MATH 120/6.0 or MATH 121/6.0 or MATH 122/6.0 or MATH 124/3.0.**COREQUISITE** MATH 221/3.0 or MATH 280/3.0.**EXCLUSION** No more than 3.0 units from STAT 351/3.0; STAT 268/3.0.

**STAT 269**- Statistics & Probability II

Basic techniques of statistical estimation such as best unbiased estimates, moment estimates, maximum likelihood.

Bayesian methods. Hypotheses testing. Classical distributions such as the t‐distribution, F‐distribution, beta distribution.

These methods will be illustrated by simple linear regression. Statistical computing.**LEARNING HOURS** 120 (36L;84P)**PREREQUISITE** (MATH 221/3.0 or MATH 280/3.0) and (STAT 268/3.0 or STAT 351/3.0), or permission of the Department.

**300 Level Courses**

**STAT 351**- Probability I

Probability theory; probability models; random variables; jointly distributed random variables; transformations and

generating functions. Inequalities and limit laws. Distributions: binomial, Poisson, exponential, gamma, normal.

Applications: elementary stochastic processes, time‐to‐failure models, binary communication channels with Gaussian noise.**LEARNING HOURS** 120 (36L;12T;72P)**COREQUISITE** MATH 221/3.0 or MATH 280/3.0.**EXCLUSION** No more than 3.0 units from STAT 351/3.0; STAT 268/3.0.

**STAT 353**- Probability II

Intermediate probability theory as a basis for further study in mathematical statistics and stochastic processes; probability

measures, expectations; modes of convergence of sequences of random variables; conditional expectations; independent

systems of random variables; Gaussian systems; characteristic functions; Law of large numbers, Central limit theory; some

notions of dependence.**LEARNING HOURS** 120 (36L;84P)**PREREQUISITE** (STAT 268/3.0 or STAT 351/3.0) and (MATH 110/6.0 or MATH 111/6.0 or MATH 112/3.0) and MATH 281/3.0.

**Website: ** mast.queensu.ca/~stat353/

**STAT 361**- Applied Methods in Statistics I

A detailed study of simple and multiple linear regression, residuals and model adequacy. The least squares solution for the

general linear regression model. Analysis of variance for regression and simple designed experiments; analysis of

categorical data.**LEARNING HOURS** 120 (36L;84P)**PREREQUISITE** (MATH 110/6.0 or MATH 111/6.0 or MATH 112/3.0) and (STAT 268/3.0 or STAT 351/3.0) and (STAT 263/3.0

or STAT 267/3.0 or STAT 269/3.0 or STAT 367/3.0), or permission of the Department.**EXCLUSION** No more than 3.0 units from ECON 351/3.0; STAT 361/3.0.

**STAT 362**- R for Data Science

Introduction to R, data creation and manipulation, data import and export, scripts and functions, control flow, debugging and profiling, data visualization, statistical inference, Monte Carlo methods, decision trees, support vector machines, neural network, numerical methods.

**400 Level Courses**

**STAT 455**- Stochastic Processes & Applications

Markov chains, birth and death processes, random walk problems, elementary renewal theory, Markov processes,

Brownian motion and Poisson processes, queuing theory, branching processes. Given jointly with STAT 855/3.0.**LEARNING HOURS** 120 (36L;12T;72P)**PREREQUISITE** STAT 353/3.0 or (STAT 269/3.0 or STAT 351/3.0 with permission of the Department).

**STAT 456**- Bayesian Analysis

An introduction to Bayesian analysis and decision theory; elements of decision theory; Bayesian point estimation, set estimation, and hypothesis testing; special priors; computations for Bayesian analysis.**LEARNING HOURS** 120 (36L;84P)**PREREQUISITE** STAT 463/3.0 or permission of the Department.

**STAT 457**- Statistical Computing

Introduction to the theory and application of statistical algorithms. Topics include classification, smoothing, model selection, optimization, sampling, supervised and unsupervised learning.**LEARNING HOURS** 120 (36L;84P)**PREREQUISITE** STAT 361/3.0 or ECON 351/3.0 or permission of the Department.

**STAT 462**- Computational Data Analysis

Introduction to the statistical packages SAS and R; classification; spline and smoothing spline; regularization, ridge regression,

and Lasso; model selection; resampling methods; importance sampling; Markov chain Monte Carlo; Metropolis‐Hasting

algorithm; Gibbs sampling; optimization. Given jointly with STAT 862/3.0.**LEARNING HOURS** 120 (36L;84P)**COREQUISITE** STAT 361/3.0 or ECON 351/3.0, or permission of the Department.

**STAT 463**- Fundamentals of Statistical Inference

Decision theory and Bayesian inference; principles of optimal statistical procedures; maximum likelihood principle; large

sample theory for maximum likelihood estimates; principles of hypotheses testing and the Neyman‐Pearson theory;

generalized likelihood ratio tests; the chi‐square, t, F and other distributions.**LEARNING HOURS** 132 (36L;96P)**RECOMMENDATION** STAT 353/3.0.**PREREQUISITE** STAT 269/3.0.

**STAT 464**- Discrete Time Series Analysis

Autocorrelation and autocovariance, stationarity; ARIMA models; model identification and forecasting; spectral analysis.

Applications to biological, physical and economic data.**LEARNING HOURS** 120 (36L;84P)**PREREQUISITE** STAT 361/3.0 or ECON 351/3.0, or permission of the Department.

**STAT 465**- Quality Management

An overview of the statistical and lean manufacturing tools and techniques used in the measurement and improvement of

quality in business, government and industry today. Topics include management and planning tools, Six Sigma approach,

statistical process charting, process capability analysis, measurement system analysis and factorial and fractional factorial

design of experiments.**LEARNING HOURS** 120 (36L;84P)**PREREQUISITE** STAT 263/3.0 or STAT 267/3.0 or STAT 269/3.0 or STAT 367/3.0 or permission of the Department.

**STAT 466**- Statistical Programming with SAS and Applications

Introduction to the basic knowledge in programming, data management, and exploratory data analysis using SAS software: data manipulation and management; output delivery system; advanced text file generation, statistical procedures and data analysis, macro language, structure query language, and SAS applications in clinical trial, administrative financial data.**LEARNING HOURS** 120 (36L;84P)**PREREQUISITE** (STAT 263/3.0 or STAT 269/3.0), or permission of the Department.

**STAT 471**- Sampling & Experimental Design

Simple random sampling; Unequal probability sampling; Stratified sampling; Cluster sampling; Multi‐stage sampling;

Analysis of variance and covariance; Block designs; Fractional factorial designs; Split‐plot designs; Response surface

methodology; Robust parameter designs for products and process improvement. Offered jointly with STAT 871/3.0.**LEARNING HOURS** 120 (36L;84P)**PREREQUISITE** (STAT 361/3.0 or ECON 351/3.0) and STAT 463/3.0 or permission of the Department.**EQUIVALENCY** STAT 362/3.0.

**STAT 473**- Generalized Linear Models

An introduction to advanced regression methods for binary, categorical, and count data. Major topics include maximumlikelihood method, binomial and Poisson regression, contingency tables, log linear models, and random effect models.

The generalized linear models will be discussed both in theory and in applications to real data from a variety of sources. Given

jointly with STAT 873/3.0.**LEARNING HOURS** 120 (36L;84P)**PREREQUISITE** (STAT 361/3.0 or ECON 351/3.0) and STAT 463/3.0 or permission of the Department.

**STAT 486**- Survival Analysis

Introduces the theory and application of survival analysis: survival distributions and their applications, parametric and

nonparametric methods, proportional hazards models, counting process and proportional hazards regression, planning and

designing clinical trials. Given jointly with STAT 886/3.0.**LEARNING HOURS** 120 (36L;84P)**RECOMMENDATION** STAT 462/3.0.**PREREQUISITE** (STAT 361/3.0 or ECON 351/3.0) and STAT 463/3.0 or permission of the Department.

**STAT 499**- Topics in Statistics

An important topic in statistics not covered in any other courses.**PREREQUISITE** Permission of the Department.**EXCLUSION** STAT 505/3.0.

**STAT 506**- Topics in Statistics II

An important topic in probability or statistics not covered in any other course.**LEARNING HOURS** 132 (24I;108P)**PREREQUISITE** Permission of the Department.